The book arrived midway last week, when I hadn’t even finished reading Tim Morton’s Hyperobjects, much less finished blogging about it. But that didn’t stop me from giving Macroanalysis a look-thru: contents, some of the figures, read a bit here and there. I ended up reading Chapter 9, “Influence”, first; I’d read Matt Wilkins’ review in the LA Review of Books:

It’s a nifty approach that produces a fascinatingly opaque result: Tristram Shandy, Laurence Sterne’s famously odd 18th-century bildungsroman, is judged to be the most influential member of the collection, followed by George Gissing’s unremarkable The Whirlpool (1897) and Benjamin Disraeli’s decidedly minor romance Venetia (1837). If you can make sense of this result, you’re ahead of Jockers himself, who more or less throws up his hands and ends both the chapter and the analytical portion of the book a paragraph later.

Would I be able to make sense of those results? thought I to myself as I read. Nope, I couldn’t. Better luck next time.

I am now prepared to offer a re-interpretation of those results. But before I do that I need to explain more or less what Jockers is doing in this the final analytical chapter of the book. How does he operationalize the concept of influence?

When we say that, for example, that J. K. Rowling was influenced by the Narnia novels of C. S. Lewis, what do we mean? We mean that she read them and has incorporated features of those books into her own work. There is a direct relationship between Rowling’s activities and those influential books.

Influence thus understood is something that ‘travels’ along certain paths in the enormous meshwork of reading and writing transactions that constitute literary culture. As there are only a relatively few writers in that network, and only a relatively few of their transactions are writing ones (let’s say that the writing of a book is a single transaction) most of the transactions in the network are readings. Only a few of the transactions in the meshwork carry influence.

But Jockers doesn’t have access to that meshwork. None of us does. To be sure, we can see bits and pieces of it here in there in diaries, letters, and published reviews, but most of the transactions are lost to history. We can only look for the effects of those transactions.

And that’s what Jockers does. He assumes, reasonably enough, that if one author is influenced by another, then we should see indicators of that influence in the work. There should be a noticeable resemblance between those works.

And that is something Jockers can look for. For each of his 3,346 texts he’s got a bunch of features, stylistic and thematic. Once he’s tossed out the uninterpretable thematic features he’s left with 578 features for each text. He then represents this information as a geometric space with 578 dimensions, one for each feature, in which we have 3346 points, one for each text. He can now calculate the distance between any two texts, that is, points, in this high dimensional feature space. That distance is a measure of the similarity between the texts.

That’s what he does, and he gives us examples of the results. For each of Pride and Prejudice, Tale of Two Cities, and Moby Dick he gives us a table listing the ten novels the shortest distance from them and thus most like them (in terms of these 578 features). Not surprisingly, other books by Austen, Dickens, and Melville respectively occupy the top slots on these similarity lists. For the Austen list, the other authors are female, but one (Thomas Lister). Similarly, the authors most like Dickens are male, though the author of Life’s Masquerade (10th) is unknown, hence gender unknown. All the authors on these two lists are British. In Melville’s case, the list is also all-male, but not all-American. Two Scots, Robert Ballantyne and Robert Louis Stevenson, also made the list.

As interesting as this is, Jockers points out that it’s a bit small scale. We need something else if we want to gauge influence throughout the century. What to do?

Why not put all the texts into a network representation (aka a graph) and track influence in the network? Easier said than done. Here’s a simple graph with seven nodes (texts):

We can think of the edges between the nodes as being proportional to the distance between the texts in ‘feature space.’ Jockers’ graph, of course, has 3346 nodes and a blither of edges between them. The closest nodes are 0.06 units apart while the most distant are 107.4 units apart, with 10.5 being the average distance (p. 163).

I’m skipping over a number of details (which you can find on pages 162-165), but the resulting graph looks like this (which is Figure 9.3, p. 165; color version from the web):

What’s remarkable about this graph is that the nodes are ordered in time from left (oldest) to right, but there is no temporal information in the data from which it was derived: “Books are being pulled together (and pushed apart) based on the similarity of their computed stylistic and thematic distances from each other” (p. 164). That temporal ordering is simply a side effect of ordering by thematic and stylistic similarity.

The chronological alignment reveals that thematic and stylistic change does occur over time. The themes that writers employ and the high-frequency function words they use to build the frameworks for their themes are nearly, but not always, tethered in time. At this macro scale, style and theme are observed to evolve chronologically, and most books are authors in this network cluster into communities with their chronological peers. Not every book and not every author is a slave to his or her epoch.

Jockers goes on to discuss some oddities about information these graphs, but I’m going to skip that, noting only that, as Jockers says, you need a large screen and interactive access to really explore patterns in the graph.

Let’s go to his final demonstration (pp. 167-168):

Node-centrality measures can provide a sense of a book’s importance to and within the larger network. The Gephi software provides tools for calculating these an many other measures, and through such measures Gephi gives us the power to sift and rank the relative importance of one node versus another....When applied to this nineteenth-century corpus in which the links are measures of stylistic and thematic affinity, the algorithm points us first to Laurence Sterne’s Tristram Shandy, next to George Gissing’s novel The Whirlpool, and then to Benjamin Disraeli’s Venetia. Tristram Shandy is a book frequently lauded as one of the highest achievements of the novel form, and, by all accounts, Gissing was one of the century’s most accomplished stylists. Disraeli’s minor novel Venetia is harder to understand. Maybe its presence here is a sign that the method had failed, or perhaps it is a sign that close readers need to reevaluate Venetia. In short, these network data are rich–too rich, in fact, to take much further in these pages because they demand that we follow every macroscale observation with a full-circle return to careful, sustained, close-reading. This is work for the future.

I agree, the data are rich, we need more and better ‘close’ reading, and there is much work for the future.

Before we do that, however, we need to prepare ourselves by thinking carefully about what Jockers has done and what his data show. For it is not clear to me that they demonstrate influence in a direct way. Our ordinary concept of influence implies reading: J. K. Rowling could have been influenced by C. S. Lewis only because she had read his books before writing her own. But, as I pointed out earlier, Jockers’ data has no information about what these authors read. He’s using stylistic and thematic similarity as a proxy measure for who read whom.

That’s a reasonable thing to do given that similarity is one ‘natural’ result of influential reading (avoidance of conscious similarity is another, somewhat different, result, one that interests Harold Bloom). But, as the song says, it ain’t necessarily so. Consider the following diagram, which follows three traits (red, green, and blue) through three generations of texts:

Each text in the third generation exhibits all three traits. One text in the second generation exhibits the three traits while the other texts exhibit only two of them. The first generation texts exhibit only one trait each. (If you wish, you can imagine all of these texts as exhibiting other traits not shown in the diagram.)

The edges in the graph indicate where a given trait came from and so are about which texts were read by the authors of the second and third generation texts. Notice that none of the third generation texts got their traits from the lone second-generation text that exhibits all three traits. The authors of those three texts obviously did not read that second generation text. Yet, by Jockers’ similarity measure, it would show up as highly similar to those three texts.

What that implies is that it is possible that none of the authors who wrote the text most similar to Disraeli’s Venetia actually read the book. Rather, they may have adopted those traits from other books that they read. In this reading of the data, then, Disraeli’s book is a kind of least common denominator rather than an unheralded masterpiece.

Never having read it, I don’t really know, but I rather suspect that it’s not a masterpiece. Whether or not it was widely read and imitated, I don’t know and have no opinion. But, as the diagram indicates, that many subsequent books are like it doesn’t imply that the others of those books got those traits from having read Disraeli. Some of them may have done so, but that’s not a necessary implication of the data.

As for whether or not Jockers’ method failed in this case, well I suspect that it has. But that’s a lazy and unimaginative interpretation of what he’s done. That node centrality produced both an acknowledged masterpiece, Tristram Shandy, and a mediocrity, Venetia, in an assessment of overall stylistic and thematic similarity in a large corpus of 19th Century novels, that strikes me as something of extraordinary interest worthy of further investigation. I say Godspeed to all those scholars who are going to be looking more deeply, and more broadly, into these issues.

Finally, the fact that a graph of the corpus–3346 texts, remember–that is based on similarity information, that that graph should sort itself into temporal order implies that the literary system has strong internal dynamic coherence. It’s not going to be pushed hither and yon by external circumstances, though it does react to them. This bears on Edward Said’s concern for the autonomy of the aesthetic sphere. No, the literary system is NOT isolated from the world; it is not autonomous in that utopian and uninteresting sense. Rather, we must understand the literary system as itself being a force in the world, one that has effects in and on the world.